Deep Learning for Problems Others Can't Solve
Some problems are too hard for standard tools. Raw signals, odd images, technical speech — for those we design and train a custom model.
Custom Architectures
A model shaped around your problem, not a stock one.
Domain-Trained
Trained on your data, in your words, for your edge cases.
Optimised for Deployment
Shrunk to run on the hardware and budget you have.
Rigorously Evaluated
Measured against a simpler model you can check.
Our Deep Learning Capabilities
Custom Model Development
CNNs, transformers and hybrid designs.
Transfer Learning
We adapt a large trained model to your field.
Edge Optimisation
Shrink a model to run on a small edge box.
Speech & Audio Models
Speech to text, speaker split and sound tagging.
Time-Series & Sensor Models
Learn from sensor data sampled many times a second.
Research & Benchmarking
We test new methods against the one you run now.
Where This Creates Impact
- Accuracy Gain Over Baseline
- 15–30%Accuracy Gain Over Baseline
- Smaller Models After Optimisation
- 10xSmaller Models After Optimisation
- Inference on Edge Hardware
- Real-timeInference on Edge Hardware
- Model Ownership Retained
- 100%Model Ownership Retained
Figures are typical ranges observed across comparable engagements, not guaranteed outcomes.
Popular Use Cases
Specialised Visual Inspection
Faults a stock model cannot see.
Domain Speech Recognition
Transcripts that get your technical terms right.
Sensor Signal Analysis
Learn what a failing motor sounds like.
On-Device Intelligence
Run it on site, with no network at all.
How This Actually Works
We use deep learning when the input is raw: images, audio, sensor signal, free text. It is also right when the pattern is too complex to describe by hand. For tabular data we would tell you to use something simpler, because it usually wins.
A custom design is trained on your own data. We then compress it through quantisation and distillation. That way it runs within your real hardware and speed limits, not only on a research GPU.
What You Get
- A custom model design built for your problem
- A written record of how it was trained and what it was trained on
- Measured performance against simpler baselines
- An optimised model sized for your target hardware
- Handover terms agreed in the contract before work starts
When This Is Not the Right Fit
We would rather tell you now than three weeks into a project.
- Your data is tabular — gradient boosting will likely beat it and cost far less to run
- A ready-made or fine-tuned model gets you close enough — we check that first
- There is no labelled data and no real way to produce any
How We Deliver
- 01
Discover
Understand your business, data, challenges and goals.
- 02
Design
Prioritise use cases and design the solution architecture.
- 03
Build
Develop, train and validate models against real outcomes.
- 04
Deploy
Integrate into your systems with monitoring and governance.
- 05
Optimise
Measure, refine and scale what demonstrably works.
Deep Learning — Common Questions
When the input is raw: images, audio, sensor signal, free text. Or when the pattern is too complex to describe by hand. For tabular business data, gradient boosting usually beats deep learning and costs far less to run.
The working system, the documentation, and training so your team can run it. Your data stays yours, and you can export it whenever you ask. Anything beyond that — model files, source, licence terms — is agreed in the contract before we start. You know exactly what you are getting.
Often, once we shrink it. Quantisation and distillation cut the size a long way, with little loss of accuracy. That is what makes edge and on-site running possible.
Longer than fine-tuning one that exists. Two to four months is normal, including data prep, training and testing. We would always check first whether a ready-made model gets you close enough.
Have a hard problem worth solving?
Book a 30–45 minute discovery call. No commitment — just a clear view of what is realistic for your business.
Book a Free Consultation